Artificial Intelligence in Wound Care: A Narrative Review of the Currently Available Mobile Apps for Automatic Ulcer Segmentation
<b>Introduction:</b> Chronic ulcers significantly burden healthcare systems, requiring precise measurement and assessment for effective treatment. Traditional methods, such as manual segmentation, are time-consuming and error-prone. This review evaluates the potential of artificial intel...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2673-7426/4/4/126 |
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| author | Davide Griffa Alessio Natale Yuri Merli Michela Starace Nico Curti Martina Mussi Gastone Castellani Davide Melandri Bianca Maria Piraccini Corrado Zengarini |
| author_facet | Davide Griffa Alessio Natale Yuri Merli Michela Starace Nico Curti Martina Mussi Gastone Castellani Davide Melandri Bianca Maria Piraccini Corrado Zengarini |
| author_sort | Davide Griffa |
| collection | DOAJ |
| description | <b>Introduction:</b> Chronic ulcers significantly burden healthcare systems, requiring precise measurement and assessment for effective treatment. Traditional methods, such as manual segmentation, are time-consuming and error-prone. This review evaluates the potential of artificial intelligence AI-powered mobile apps for automated ulcer segmentation and their application in clinical settings. <b>Methods:</b> A comprehensive literature search was conducted across PubMed, CINAHL, Cochrane, and Google Scholar databases. The review focused on mobile apps that use fully automatic AI algorithms for wound segmentation. Apps requiring additional hardware or needing more technical documentation were excluded. Vital technological features, clinical validation, and usability were analysed. <b>Results:</b> Ten mobile apps were identified, showing varying levels of segmentation accuracy and clinical validation. However, many apps did not publish sufficient information on the segmentation methods or algorithms used, and most lacked details on the databases employed for training their AI models. Additionally, several apps were unavailable in public repositories, limiting their accessibility and independent evaluation. These factors challenge their integration into clinical practice despite promising preliminary results. <b>Discussion:</b> AI-powered mobile apps offer significant potential for improving wound care by enhancing diagnostic accuracy and reducing the burden on healthcare professionals. Nonetheless, the lack of transparency regarding segmentation techniques, unpublished databases, and the limited availability of many apps in public repositories remain substantial barriers to widespread clinical adoption. <b>Conclusions:</b> AI-driven mobile apps for ulcer segmentation could revolutionise chronic wound management. However, overcoming limitations related to transparency, data availability, and accessibility is essential for their successful integration into healthcare systems. |
| format | Article |
| id | doaj-art-00b8d931cc9c4bb086622e277374c8c5 |
| institution | Kabale University |
| issn | 2673-7426 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioMedInformatics |
| spelling | doaj-art-00b8d931cc9c4bb086622e277374c8c52024-12-27T14:13:20ZengMDPI AGBioMedInformatics2673-74262024-12-01442321233710.3390/biomedinformatics4040126Artificial Intelligence in Wound Care: A Narrative Review of the Currently Available Mobile Apps for Automatic Ulcer SegmentationDavide Griffa0Alessio Natale1Yuri Merli2Michela Starace3Nico Curti4Martina Mussi5Gastone Castellani6Davide Melandri7Bianca Maria Piraccini8Corrado Zengarini9Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, ItalyDepartment of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, ItalyDepartment of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, ItalyDepartment of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, ItalyDepartment of Physics and Astronomy, University of Bologna, 40138 Bologna, ItalyDepartment of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, ItalyDepartment of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, ItalyDepartment of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, ItalyDepartment of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, ItalyDepartment of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy<b>Introduction:</b> Chronic ulcers significantly burden healthcare systems, requiring precise measurement and assessment for effective treatment. Traditional methods, such as manual segmentation, are time-consuming and error-prone. This review evaluates the potential of artificial intelligence AI-powered mobile apps for automated ulcer segmentation and their application in clinical settings. <b>Methods:</b> A comprehensive literature search was conducted across PubMed, CINAHL, Cochrane, and Google Scholar databases. The review focused on mobile apps that use fully automatic AI algorithms for wound segmentation. Apps requiring additional hardware or needing more technical documentation were excluded. Vital technological features, clinical validation, and usability were analysed. <b>Results:</b> Ten mobile apps were identified, showing varying levels of segmentation accuracy and clinical validation. However, many apps did not publish sufficient information on the segmentation methods or algorithms used, and most lacked details on the databases employed for training their AI models. Additionally, several apps were unavailable in public repositories, limiting their accessibility and independent evaluation. These factors challenge their integration into clinical practice despite promising preliminary results. <b>Discussion:</b> AI-powered mobile apps offer significant potential for improving wound care by enhancing diagnostic accuracy and reducing the burden on healthcare professionals. Nonetheless, the lack of transparency regarding segmentation techniques, unpublished databases, and the limited availability of many apps in public repositories remain substantial barriers to widespread clinical adoption. <b>Conclusions:</b> AI-driven mobile apps for ulcer segmentation could revolutionise chronic wound management. However, overcoming limitations related to transparency, data availability, and accessibility is essential for their successful integration into healthcare systems.https://www.mdpi.com/2673-7426/4/4/126artificial intelligenceimage segmentationmobile appwound caredeep learning |
| spellingShingle | Davide Griffa Alessio Natale Yuri Merli Michela Starace Nico Curti Martina Mussi Gastone Castellani Davide Melandri Bianca Maria Piraccini Corrado Zengarini Artificial Intelligence in Wound Care: A Narrative Review of the Currently Available Mobile Apps for Automatic Ulcer Segmentation BioMedInformatics artificial intelligence image segmentation mobile app wound care deep learning |
| title | Artificial Intelligence in Wound Care: A Narrative Review of the Currently Available Mobile Apps for Automatic Ulcer Segmentation |
| title_full | Artificial Intelligence in Wound Care: A Narrative Review of the Currently Available Mobile Apps for Automatic Ulcer Segmentation |
| title_fullStr | Artificial Intelligence in Wound Care: A Narrative Review of the Currently Available Mobile Apps for Automatic Ulcer Segmentation |
| title_full_unstemmed | Artificial Intelligence in Wound Care: A Narrative Review of the Currently Available Mobile Apps for Automatic Ulcer Segmentation |
| title_short | Artificial Intelligence in Wound Care: A Narrative Review of the Currently Available Mobile Apps for Automatic Ulcer Segmentation |
| title_sort | artificial intelligence in wound care a narrative review of the currently available mobile apps for automatic ulcer segmentation |
| topic | artificial intelligence image segmentation mobile app wound care deep learning |
| url | https://www.mdpi.com/2673-7426/4/4/126 |
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